Driving Innovation Across
Dual Dimensions
Merging Virtual with Physical,
Empowering AI with Physics,
Augmenting Sensors with Simulation
Introduction
At Duoverse, we are pioneering the duo paradigm and redefining the metaverse as a physics-aware and AI-powered ecosystem, bridging critical gaps in technology and innovation.
In an era where scientific precision, technological efficiency, and adaptive solutions drive progress, our approach seamlessly integrates dual elements: virtual and physical worlds, physics and AI algorithms, simulated and measured data.
This unique integration enriches and enhances both R&D efforts and world applications, empowering businesses to operate smarter, faster, and more sustainably.
What do we mean by Duo?
Duoverse operates across multiple levels, each addressing distinct yet interconnected aspects of innovation.
At its core, the first level focuses on the conceptual synergy between the virtual and physical worlds, providing a foundation for the digitalization of physical assets. This is where digital replicas, or digital twins, come into play, enabling real-time interaction with physical systems to optimize their performance. As a result, businesses can significantly enhance performance while reducing operational downtime, gaining the ability to make informed decisions faster, backed by a seamless digital-physical interaction.
Building upon this, the second level dives deeper into the technical domain, where AI and physics are combined to embrace the complexities of the digitalized assets. This integration delivers more accurate outcomes with fewer computational resources, reducing the energy consumption while maintaining high performance. The frugal nature of this approach not only enhances operational efficiency but also makes advanced AI solutions more accessible, scalable, and sustainable. As AI models are enriched with physics-driven insights, businesses benefit from a smarter and more responsible deployment of technology.
At the third level, Duoverse shifts to an operational focus, augmenting sensors with simulations. By leveraging fast and robust physics and AI-based virtual data, Duoverse bridges gaps in experimental data. This approach reduces dependence on resource-intensive experiments and ensures data completion in conditions where sensor placement is challenging or impossible. By simulating and compensating for missing data, Duoverse accelerates innovation, providing companies with accurate, enriched insights to unlock new real-world applications.
All these levels are essential for developing a sustainable, physics-aware metaverse, aligning with Duoverse's commitment to driving transformative solutions for industry actors seeking to thrive in an increasingly complex digital landscape.
1. Virtual and Physical: Bridging the Digital and Real Worlds
The focus on seamlessly connecting virtual models to their physical counterparts through the creation of digital twins, which are digital replicas of physical assets continuously updated with real-time data. These are meant to allow businesses to monitor, predict, and optimize the performance of their systems. A digital twin is key in industries like digital manufacturing, where the ability to simulate and adjust production chains in real-time can dramatically enhance efficiency and reduce downtime.
In a manufacturing setting, for instance, a digital twin could represent an entire production line, integrating data from Internet of Things (IoT) sensors placed on machinery. This real-time data allows for immediate adjustments, predictive maintenance, and optimization of production flow, reducing delays and preventing breakdowns. Digital twins also support scenario testing—companies can simulate production changes or new equipment integration without halting the physical process, ensuring smoother transitions and minimizing disruptions.
Moreover, in broader infrastructure projects, such as smart buildings, digital twins offer a way to optimize energy use, monitor structural health, and manage maintenance schedules efficiently. The integration of Computer Aided Engineering (CAE) tools and IoT further strengthens the connection between the physical and digital worlds, ensuring that these systems continuously learn and improve over time.
By providing real-time insights and offering precise control over physical systems, we enable faster, data-driven decisions, minimizing operational inefficiencies, and enhancing the sustainability of business operations across various industries.
2. AI with Physics: Making AI Frugal with Physics Insights
We integrate physics and mathematical knowledge directly into AI models, enhancing both their frugality and efficiency. Traditional machine learning and deep learning approaches often require vast amounts of data and computational resources. By embedding physics-based principles—such as those found in Reduced Order Models (ROMs), Physics-Informed Neural Networks (PINNs), Graph Neural Networks (GNNs), and in other branches of Computational Sciences —into AI models, we can significantly reduce the computational burden while maintaining high levels of precision and reliability. This frugality allows our AI to operate efficiently on lower computational resources, minimizing energy consumption and making advanced AI more accessible for businesses of all sizes.
A basic example, well-known in informed machine learning, may be the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which identifies simplified mathematical models from data by selecting only the essential terms in complex systems (Figure 5). This approach enables efficient, interpretable models that retain key physical insights, making it particularly useful for systems governed by underlying physics.
At Duoverse, our physics-enhanced AI models go beyond brute-force data processing. By grounding predictions in real-world principles, we eliminate the need for massive datasets and long training times typical of traditional machine learning techniques. This leads to faster, more efficient algorithms that require fewer resources, making AI deployment not only more affordable but also scalable and sustainable across industries. The inclusion of physics-driven insights ensures that our models provide high-fidelity results while consuming significantly less data.
At Duoverse, the lightweight nature of our models means they can be efficiently embedded into low-power devices, enabling real-time decision-making at the edge, without the need for constant cloud computing support. This makes our AI solutions ideal for edge computing environments, offering both cost-effective and environmentally friendly alternatives to traditional AI systems. Businesses can now enjoy the advantages of rapid evaluations and real-time performance with models that are frugal in data usage, fast in execution, and scalable for a wide range of applications.
3. Measured and Simulated Data: Enriching Experimental Data with Physics-Knowledge-DrivenVirtual Data
We focus on enriching experimental data through the integration of physics-driven virtual data. By incorporating advanced physics knowledge into our models, we generate synthetic data that complements real-world experimental data, a process known as data augmentation. This approach significantly increases the accuracy and robustness of AI models, especially when experimental data is scarce or incomplete.
A key enabler of this is our reliable real-time simulators, which can generate vast amounts of data on demand. These simulators can produce millions of physically consistent responses in seconds, providing an invaluable tool for data completion and augmentation.
The integration options for these AI models, whether through FMU standards, direct API integration, or custom blocks in Simulink, ensure they work seamlessly within simulation-based software, accelerating the design process and adding real-time insights in operational phase.
During the design stage, this capability is especially crucial, for instance, for structural and topology optimization. By utilizing parametric models, engineers can explore extensive design scenarios and pick the optimal (efficient and resilient) configuration, without the need for physical prototypes. Furthermore, parametric models can be employed for calibration, particularly for fine-tuning complex material models to closely match experimental data, reducing the need for extensive physical testing.
In the operational phase, real-time simulators are vital for monitoring, control, and fault identification. Businesses can anticipate failures, perform informed maintenance, and make optimal decisions quickly, minimizing downtime.
Our lightweight AI models are designed for direct embedding in machines, providing local real-time performance monitoring and fault detection. This embedded approach enhances operational efficiency and data security by eliminating the need to transmit sensitive information to external servers, keeping everything within a secure, on-premises workflow.
The Benefits: Transforming R&D and Beyond
At Duoverse, we empower businesses acting at various levels of the value chain, from research and development to real-world implementation. By combining simulations with experimental data, we enable faster prototyping, testing, and iteration, which accelerates innovation, enhances product development and reduces time to market.
Efficiency, precision, and frugality are core strengths of our technology. By embedding physics and mathematics into AI, we minimize computational loads while maintaining high accuracy, allowing for smarter operations with fewer resources. This frugal innovation ensures that businesses can optimize their processes in a cost-effective and precise manner, adapting to varying needs along the value chain.
Moreover, our technology supports sustainable and scalable solutions. With lightweight models that run efficiently on edge devices, we reduce energy consumption and enable large-scale deployment without compromising environmental goals. Businesses can integrate these solutions at different operational levels, ensuring scalability while remain inaligned with sustainability objectives.
Finally, trust and transparency are embedded in our approach. Our physics-aware AI models provide clear, interpretable insights into how predictions are made, which fosters trust among stakeholders.
Conclusion
In summary, at Duoverse, the duo paradigm stands as the foundation of our innovation strategy, merging virtual with physical, empowering AI with physics, augmenting sensors with simulation.
We create frugal, scalable, and efficient solutions that empower businesses to innovate, optimize, and scale with confidence
We bridge the gap between models and reality, driving a new era of sustainable, precise, and intelligent solutions.
Contact Us
For more information about our solutions and how we can transform your business, please contact us at contact@duoverse.ai.
